an interdisciplinary perspective on artificial immune systems jon timmis department of electronics...
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An Interdisciplinary Perspective on Artificial
Immune Systems
Jon TimmisDepartment of Electronics andDepartment of Computer [email protected]://www-users.cs.york.ac.uk/jtimmis
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Artificial what?
Artificial Immune Systems: A typical definition
AIS are adaptive systems inspired by theoretical immunology and observed immune functions,
principles and models, which are applied to complex problem domains
[De Castro and Timmis,2002]
But I think this might be a bit limiting in terms of definition ..
A bit of history … Developed from the field of theoretical
immunology in the mid 1980’s. Suggested we ‘might look’ at the IS
1990 – Ishida first use of immune algorithms to solve problems
Forrest et al – Computer Security mid 1990’s Hunt et al, mid 1990’s – Machine learning ICARIS conference series, ARTIST network
History (cont.) Started quite immunologically grounded
Bersini’s work with Varela Forrest's work with Perelson
Kind of moved away from that, and abstracted more
Now there seems to be a move to go back to the roots of immunology and greater interaction … but how do we manage this interaction to make it worth
while for all concerned …. ?
What does engineering have to do with immune systems?
Unique to individuals Distributed Imperfect Detection Anomaly Detection Learning/Adaptation Memory Feature Extraction Diverse ..and more
Robust Scalable Flexible Exhibit graceful
degradation Homeostatic
Systems that are:Computational Properties
Example Application Areas
Computer Security
Computer Security
OptimisationOptimisationRobotic Control
Robotic Control
Data-Mining and
classification
Data-Mining and
classification
Anomaly Detection
Anomaly Detection
Network models
Clonal Selection
Negative selection
Bone Marrow
What is the Immune System ?
a complex system of cellular and molecular components having the primary function of distinguishing self from not self and defense against foreign organisms or substances (Dorland's Illustrated Medical Dictionary)
The immune system is a cognitive system whose primary role is to provide body maintenance (Cohen)
Immunologists Disagree“There is an obvious and dangerous
potential for the immune system to kill its host; but it is equally obvious that the best minds in
immunology are far from agreement on how the immune system manages to avoid
this problem”
Langman, R. E. and Cohn, M., Editorial Summary, Seminars in Immunology, vol. 12, pp. 343-344, 2000
What is the Immune System ?
S/NS
Cohen
Varela
Matzinger
• The are many different viewpoints
•Lots of common ingredients (??)
•All tell us about information processing …
Clonal Selection as an example for information processing
Immune Responses - continual information processing
Antigen Ag 1 Antigens Ag1, Ag2
Primary Response Secondary Response
Lag
Response to Ag1
Anti
body Concentration
Time
Lag
Response to Ag2
Response to Ag1
...
...
Cross-Reactive Response
...
...
Antigen Ag1 + Ag3
Response to Ag1 + Ag3
Lag
An `artificial immune system’ in an engineering
contextKeeping ATM’s working
ATMs High usage machines Don’t go wrong that often, but if they do it can
be expensive Create logs when they go wrong It is possible to use that data to immunise a
system at a number of levels via an Adaptable Error Detection system
Adaptable error detection as a means to improved availability Error detection
Improved error detection enhances availability Error detection techniques usually exploit known
systems profile for detecting error states and behaviour These error detection techniques are limited to the
detection of errors known at design-time of systems Adaptable error detection is aimed at detecting errors
that were not known during the design-time of systems
A Framework for AIS
Algorithms
Affinity
Representation
Application
Solution
AIS
[De Castro and Timmis, 2002]
Within the AIS Framework
Representation Sequence of states --> fatal state
Affinity measure Similarity of sequences (weighted)
Algorithm Dynamic clonal selection
[De Lemos et al, 2007]
Architecture for Immune AED
[De Lemos et al, 2007]
Results
AISEC v1 AISEC v2
Accuracy
Mean detectionTime interval
85.78%(6)89.93%(.2)
86.67%(5)91.53%(.16)
0:11:21:22(0:5:20:16) 0:01:03:30 (0:0:9:35)
0:12:31:10 (0:3:36:37)
0:02:25:41 (0:0:6:16)
[De Lemos et al, 2007]
A bit of time for reflection …
Are we really capturing immune system complexity in our AIS?(or should we even care?)
modelling
Analyticalframework/
principle
A Framework for Thinking about and Developing AIS
Biologicalsystem
Simplifyingabstract
representation
Bio-inspiredalgorithms
Probes,Observations,experiments
DC activation, T-cell clonality
Mathematical models
Construct a computational
model
Abstract into algorithms
suitable for an application
Analysable, validated systems that fully exploit the underlying biology
[Stepney et al, 2005]
Interdisciplinary interaction via immune
modelling
What is in it for both sides?
Modelling Approaches Mathematical
E.g. Differential equations Computational
Various calculi Agent based modelling UML
We are investigating a number of different approaches at the moment to see which (if any) are useful (both to us and immunologists)
UML UML = Unified Modelling Language
Collection of 13 diagrams for general purpose modelling
Mostly used in software engineering for modelling “the real world”...
Diagrams fall into 2 categories Structural Behavioural
Modelling Complex Systems with UML Most of the diagrams in UML we
have not found to be that useful Ones that we have:
Class diagrams: what things are State diagrams: how things behave Activity diagrams: how things interact
UML Perspectives Conceptual
Concepts of the domain Implementing classes are related, but doesn't
have to be one-to-one mapping Specification
Interfaces Implementation
Code specifics
State Chart - Clonal Selection
[Bersini, 2006]
Process Oriented Approaches Processes are again a natural way to think about
biological systems Investigating two approaches of modelling this way Current research is investigating the development of a
pattern language for complex systems (at many levels) Modelling infrastructure (tool set, and method) for the
modelling of complex systems - our drive is the immune system
Occam- is our target language which allows us to build large-scale, highly parallel simulation
Currently working with the IIU at York on the development of models of expansion and contraction of blood vessels in lymph nodes and also the formation of granulomas under certain infections (also making use of UML in this context)
Extensible Architecture for Homeostasis
http://www.bioinspired.com/research/xArcH/index.shtml
-Calculus The -calculus [Milner 1999]. A
process calculus designed to model communicating mobile systems.
What is mobility?
Stochastic -Calculus• -calculus is good for qualitative analysis of
systems, Stochastic allows quantitative.• Associates every activity with a rate parameter
r [0, ].
Why use -Calculus? Can model the interactions between biological
components directly, possibly more intuitive (in some cases) than ODE modeling.
Can perform qualitative analysis through their bi-simulation equivalence.
Can perform quantitative analysis through simulation SPiM, BioSpi.
Through analysis can hopefully abstract what it is about the biological system that gives it its behaviour.
Some interesting immunology: Tunable T-cell receptors Classic immunology suggests a clear recognition of self/non-self
by randomly generated repertoire of cells - how is this possible? Tunable activation threshold (TAT): Proposed by [Grossman,
1992] to help explain mechanisms for self-tolerance. T Cells are mostly discussed and are viewed as having tunable
thresholds with which dictate proliferation and differentiation and therefore react only to changes in the environment and not any one specific interaction
The implications are: Self-reactive T-cells can exist but …. .. they require generally higher affinity for antigen, or a higher
avidity is required, i.e. the rate and amount by at which peptides are presented is faster for antigen.
One small part … Excitatory and Inhibitory factors are produced when the T cell
binds via its T Cell Receptor A war of phosphorylation between a kinase and a phosphatase. If
kinase activity is higher than phosphatase causes phosphorylation. If phosphatase activity is higher than kinase
causes dephosphorylation.
Why might this TAT idea be useful to engineers? The real-world is hard, and building systems that
can cope with a variety of input, that changes over time, is difficult
If we could have a system of agents that can tune themselves to tolerate, or not, certain input .. that would be very useful .. It would allow us to to begin to capture homeostasis ….
Look at patterns of response
Lymphocyte Entry to the Lymph Node through High Endothelial Venules
http://www.cosmos-research.org
On-going modelling work Collaboration with the Infection and
Immunology Unit at York Early stages (no simulation as yet, still
under development), have some basic models
Provide support for the hypothesis: The increase in lymphocyte numbers in lymph
node during an immune response is a direct result of migration rather than proliferation of existing lymphocytes in the lymph node
38
Lymph Nodes
Immune organs where adaptive immune response initiated and antibodies produced
Hundreds throughout body
Cells enter though blood or lymphatic system
39
Venules
Small blood vessels Bring de-oxygenated blood to the
veins from capillary bed
40
High Endothelial Venules (HEV) Certain areas of the lymph node
venule network are made up of HEVs HEVs characterised by tall and plump
endothelial cells
Endothelial CellEndothelial Cell
41
HEVs in a Lymph Node
42
Pericytes
Cells that wrap around small blood vessels Act as scaffolding Similar to smooth muscle cells
Constriction and dilation regulates diameter and blood flow of vessel
Endothelial Cell
Pericyte
43
Lymphocyte Migration (1)
Lymphocytes enter lymph node through HEVs Initiate in a rolling process Under certain conditions, lymphocytes slow
and squeeze though between endothelial cells
44
Lymphocyte Migration (2) Rolling, slowing and migration mechanism
controlled by cell surface molecules and receptors (selectins, integrins, chemokines)
45
Lymphocyte Migration (3)
A chemical signal molecule (chemokine) emitted in HEV crucial to lymphocyte migration HEVs facilitate lymphocytes migration but
exclude other leukocytes (white blood cells) Quarter of circulating lymphocytes leave
blood after entering HEV Migration through venule takes between
10 and 20 minutes
46
Number of cellsin millions
Experimental data
Our immunologists have measured Number of lymphocytes in a node during
response Relationship between pericyte dilation
(distance from vessel) and blood vessel size
47Lumen Size in nm Venule Perimeter in nm
PericyteDistancein nm
What are we doing with this? Developing UML models of the rolling process
For the most part this has been done. Developing simulations
First without space, then with space Output will be (in the first instance) a graph showing
lymphocyte numbers over time Number of challenges
• Time, space etc. Importantly, we are reviewing the process of modelling.
What assumptions do we make What problems do we encounter What tools work and what don’t (and why)
A wider field than ever before? Three types of ‘AIS’ people:
1. ‘Literal’ school : Those who try and build things to do what the IS does (e.g. security systems)
2. ‘metaphorical’ school: Those who use the IS as inspiration, but may be far from the what they IS actually does e.g. optimisation algorithms
3. ‘modelling’ school: Those who try and understand the IS through a series of models (computational and mathematical) e.g. models of self/non-self or tunable activation thresholds
[Cohen, 2007]
The great possibility for interaction Use of modelling tools and the
development of new tools CoSMoS project http://www.cosmos-
research.org Engage the experimentalist
They want predictions - models should be able to help
Through good modelling, engineering can also reap the benefit through a greater understanding of the immune system
References [Cohen, 2007] Computing the state of the body. Nature Rev. Imm. 7, 569-574
(2007) [De Lemos et al, 2007] R. De Lemos, J. Timmis. M. Ayara, and S. Forrest. Immune
Inspired Adaptable Error Detection for Automated Teller Machines. IEEE SMC Part B. [Forrest and Beachemin, 2007] Computer Immunology. Immunological Reviews. Vol.
216. [Timmis 2007] J. Timmis. Challenges for Artificial Immune Systems. Natural
Computation. [Stepney et al. 2006] S. Stepney, R. Smith, J. Timmis, A. Tyrrell, M. Neal and A.Hone.
Conceptual Frameworks for Artificial Immune Systems, International Journal of Unconventional Computing. 2006.
[De Castro and Timmis,2002] L. De Castro and J. Timmis. Artificial Immune Systems; A New Computational Intelligence Paradigm. Springer. 2002.
[Farmer et al, 1986] Farmer, J. D., N. H. Packard and A. Perelson. "The Immune System, Adaptation, and Machine Learning." Physica D 22(1-3) (1986): 187-204
[Owens et al,2008] Owens, N, Timmis, J. Tyrrell, A. and Greensted, A. Modelling the Tunability of Early T-cell Signaling Events. ICARIS 2008.
Acknowledgements Paul Andrews /Susan Stepney /
Amelia Ismail (CoSMoS) Lisa Scott, Mark Coles (IIU) Nick Owens / Andy Greensted / Andy
Tyrrell (Xarch)